scholarly journals Comparative Analysis of Collaborative Filtering-Based Predictors of Scores in Surveys of a Large Company

2021 ◽  
Author(s):  
Markos F. B. G. Oliveira ◽  
Myriam Delgado ◽  
Ricardo Lüders
Keyword(s):  
Comparative Analysis ◽  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Relevant Alternatives ◽  
Large Company ◽  
Item Item

Collaborative Filtering (CF) can be understood as the process of predicting the preferences of users and deriving useful patterns by studying their activities. In the survey context, it can be used to predict answers to questions as combinations of other available answers. In this paper, we aim to test five CF-based algorithms (item-item, iterative matrix factorization, neural collaborative filtering, logistic matrix factorization, and an ensemble of them) to estimate scores in four survey applications (checkpoints) composed of 700,000 employee's ratings. These data have been collected from 2019 to 2020 by a large Brazilian tech company with more than 10,000 employees. The results show that collaborative filtering approaches provide relevant alternatives to score questions of surveys. They provided good quality estimates. This result can be further explored to eventually reduce the size of questionnaires, avoiding burden phenomena faced by respondents when dealing with large surveys.

scholarly journals Kernelized Matrix Factorization for Collaborative Filtering

2016 ◽  
Author(s):  
Xinyue Liu ◽  
Chara Aggarwal ◽  
Yu-Feng Li ◽  
Xiaugnan Kong ◽  
Xinyuan Sun ◽  
...  
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization

scholarly journals Collaborative Filtering Recommendation Using Nonnegative Matrix Factorization in GPU-Accelerated Spark Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-15
Author(s):  
Bing Tang ◽  
Linyao Kang ◽  
Li Zhang ◽  
Feiyan Guo ◽  
Haiwu He
Keyword(s):  
Collaborative Filtering ◽  
Processing Speed ◽  
Matrix Factorization ◽  
Nonnegative Matrix Factorization ◽  
Nonnegative Matrix ◽  
Experimental Results ◽  
Computational Time ◽  
Data Sets ◽  
Heterogeneous Cluster ◽  
The Matrix

Nonnegative matrix factorization (NMF) has been introduced as an efficient way to reduce the complexity of data compression and its capability of extracting highly interpretable parts from data sets, and it has also been applied to various fields, such as recommendations, image analysis, and text clustering. However, as the size of the matrix increases, the processing speed of nonnegative matrix factorization is very slow. To solve this problem, this paper proposes a parallel algorithm based on GPU for NMF in Spark platform, which makes full use of the advantages of in-memory computation mode and GPU acceleration. The new GPU-accelerated NMF on Spark platform is evaluated in a 4-node Spark heterogeneous cluster using Google Compute Engine by configuring each node a NVIDIA K80 CUDA device, and experimental results indicate that it is competitive in terms of computational time against the existing solutions on a variety of matrix orders. Furthermore, a GPU-accelerated NMF-based parallel collaborative filtering (CF) algorithm is also proposed, utilizing the advantages of data dimensionality reduction and feature extraction of NMF, as well as the multicore parallel computing mode of CUDA. Using real MovieLens data sets, experimental results have shown that the parallelization of NMF-based collaborative filtering on Spark platform effectively outperforms traditional user-based and item-based CF with a higher processing speed and higher recommendation accuracy.

Proximal maximum margin matrix factorization for collaborative filtering

2017 ◽  
Vol 86 ◽  
pp. 62-67 ◽  
Author(s):  
Vikas Kumar ◽  
Arun K. Pujari ◽  
Sandeep Kumar Sahu ◽  
Venkateswara Rao Kagita ◽  
Vineet Padmanabhan
Keyword(s):  
Collaborative Filtering ◽  
Matrix Factorization ◽  
Maximum Margin
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